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Customer care is important for its role in relationship building. This role has traditionally been performed by human customer agents; however, the emergence of interactive generative AI (GenAI) shows potential for using AI for customer care in emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this article develops an AI-enabled customer care journey, from accurate emotion recognition to empathetic response, emotional management support, and, finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth interviews with top managers and a survey of chief marketing officers. By juxtaposing these requirements against the current feeling capabilities of GenAI, the authors highlight the technological challenges engineers must tackle. The article concludes with a set of marketing tenets for implementing and researching the caring machine. These include verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to enhance GenAI's emotion understanding, employing "response engineering" to personalize emotion management recommendations, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value. [ABSTRACT FROM AUTHOR]

For more than 20 years, the consumer–brand relationship (CBR) subfield has flourished with scores of constructs being employed. We provide an epistemological examination of its 14 most commonly measured relational constructs (e.g. Brand Love, Self-Brand Connection) collected from 767 research articles, reflecting 1,753 scales and approximately 9,200 items. We demonstrate that constructs overlap an average of 43% across all journals and 21% in top journals due to assessing highly similar or synonymous ideas (i.e. jangle). We use a combination of text and cluster analyses to show that measures of allegedly the same construct are polysemic, having an average of 5.3 different meanings (i.e. jingle). The results document in the CBR subfield the types of measurement inconsistencies and ambiguities that have sown confusion and frustration in other academic fields. We discuss the roots of these problems and offer recommendations aimed at helping scholars to improve measurement practices and to limit the presence of jingle and jangle in the CBR subfield. [ABSTRACT FROM AUTHOR]

Digital technologies challenge incumbent firms to rethink their established approaches to customer relationships. This article examines how a corporate bank reconfigured its relationship-oriented business model to benefit from digital transformation. The case analysis reveals a gradual transition toward a blended service model that first replaces, then complements, and finally augments physical with digital in increasingly complex customer interactions. While replacing and complementing human-enabled services with digital offerings are necessary steps of the digital transition, the associated competitive advantages are perceived as unlikely to endure. In contrast, augmenting human-enabled services with sophisticated digital technologies holds the potential for sustainable competitive advantage. [ABSTRACT FROM AUTHOR]

Routines shape many aspects of day-to-day consumption. While prior research has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which the authors define as repeated behaviors with recurring, temporal structures—for customer management. One reason for this dearth is the difficulty of measuring routines from transaction data, particularly when routines vary substantially across customers. The authors propose a new approach for doing so, which they apply in the context of ridesharing. They model customer-level routines with Bayesian nonparametric Gaussian processes, leveraging a novel kernel that allows for flexible yet precise estimation of routines. These Gaussian processes are nested in inhomogeneous Poisson processes of usage, allowing the authors to estimate customers' routines and decompose their usage into routine and nonroutine parts. They show the value of detecting routines for customer relationship management in the context of ridesharing, where they find that routines are associated with higher future usage and activity rates, and more resilience to service failures. Moreover, the authors show how these outcomes vary by the types of routines customers have, and by whether trips are part of the customer's routine, suggesting a role for routines in segmentation and targeting. [ABSTRACT FROM AUTHOR]

Embedded with artificial empathy, artificial intelligence (AI) companions (ACs) can create, communicate, and deliver value to customers. This article analyzes current and emerging ACs in order to explore AI's role in transforming customer journeys and establishing long-lasting customer relationships. It also identifies existing business challenges in developing user-AI companionship and offers a remedial strategy matrix. Purely functional or emotional capabilities lead to interest in the companion plummeting with time. The findings reveal the need to design holistic ACs with a hybrid of functional, emotional, and conversational capabilities to mitigate the uncanny valley problem. [ABSTRACT FROM AUTHOR]

Expecting improvements in productivity and performance, organizations continue to invest considerably in sales technology tools and platforms such as customer relationship management, social media, and artificial intelligence. Accordingly, scholars in this area have produced a rich literature stream. Nevertheless, a fragmented picture remains regarding sales technology's role within the sales process and the factors leading to its adoption, use, and subsequent effectiveness. Hence, synthesizing extant scholarship that can be easily approached by managers and which can offer a guide for future research represents a worthy scholarly endeavor. In this study, we comprehensively (e.g. 115 articles) review the sales technology literature covering over fifty years (1971 to 2023). In so doing, we identify themes from past research and discuss implications for sales managers in the current technological era while offering potential research avenues that speculate on the future of the sales technology landscape. [ABSTRACT FROM AUTHOR]

Despite the growing literature on consumer participation on social media, little is known about customer experiences of social media interactions with brands and the subsequent impact on full-service restaurants, an economic sector that has heavily embraced social media marketing. This study examined the direct and indirect effects of social media-mediated customer engagements with brands on restaurant customers' purchase intentions in South Africa's Free State Province. Drawing on a positivist epistemology and a quantitative research methodology, a convenience sample of 379 full-service restaurant customers was surveyed, and 307 customers responded successfully. According to the analyses, customer-to-customer (C2C) and customer to business (C2B) social media engagements positively influenced customer purchase intentions both directly and indirectly through brand equity. The study recommended interventions to augment customers purchase intentions and actual purchases in full-service restaurants, such as incorporating content creation, customer relationship management, on-the-job digital skills training, and a social media marketing strategy. [ABSTRACT FROM AUTHOR]

Information and communication technologies (ICTs) are effective in helping firms adapt to unstable external conditions. Specifically, the development of smartphones, mobile apps, social media, and payment systems have enabled both well-developed corporations and small to medium-sized enterprises to improve their performance and competitiveness. The focus of researchers and managers has shifted from the availability and functionality of ICTs to how effectively they can be used. However, there is a lack of agreement on the definition of ICT-related capabilities. Therefore, this study investigates the relationship between SMEs' ICT-related dynamic capabilities and their performance during economic stagnation. The study introduces unique constructs for ICT-related capabilities based on dynamic capability theory. The main results of the structural equation modeling procedure demonstrate that the capability to align business development with ICT implementation and CRM capabilities have a direct positive effect on SMEs' performance. Additionally, the estimated results indicate that the speed of industry change affects all ICT-related dynamic capabilities. [ABSTRACT FROM AUTHOR]

Customer relationship management (CRM) describes the standards that a company adheres to when interacting with customers. This bibliometric analysis will draw the attention of researchers toward CRM. Since the COVID-19 pandemic, CRM has become a relevant topic for all marketers, whether online or offline. This study will also help to evaluate the relationships between various articles and the degree of cooperation among various authors, universities, and countries in the field of CRM studies to determine the sustainability and trending topics in various fields of CRM and CRM strategies on customers' purchase intentions over the period 2000 to 2022. Biblioshiny, a web-based tool for bibliometric analysis, was used in this study to analyze a sample of 828 papers pulled from the Scopus database and the Web of Science between 2000 and 2022 that contained the keywords "customer relationship management," "customer relationship management strategies" to identify the development and trending topics in customer relationships. The phrase "customer relationship management" is strongly connected to many other terms like "customer relationship management strategies," "CRM," "customer satisfaction," "e-CRM," "customer loyalty," and many more that have emerged as important subtopics in this discipline over the past 2 decades. This research will be also useful for new researchers to perform more research on CRM in many different fields also. [ABSTRACT FROM AUTHOR]

This article examines paradoxes, challenges, and opportunities in the context of ethical customer experience management. Central to this discussion are different stakeholders such as firms, customers, policymakers, regulators, and society at large, as well as key issues associated with them. Firms aim to design and manage compelling experiences using customer data but face challenges balancing cost, customization, ethical concerns, and fairness in customer experience management. Customers should be encouraged to reflect on whether to share personal information for customized experiences, as well as on how far their responsibility goes in ensuring critically informed decision‐making. Policymakers and regulators must integrate the views of various stakeholders into policies and guidelines that promote a healthy society. Importantly, the digitization of customer experience has given rise to unprecedented ethical concerns related to the prospect of 'programming' these experiences. The shrinking temporal and spatial distance between stimuli, customer action, data footprint, and tracking poses significant reflections and dilemmas for both customers and firms. In this context, it is essential to determine ethical reflections and guidelines for customer experience design and management. We argue that firms should involve customers in assessing the implications of their experiences, while policymakers should facilitate the involvement of all stakeholders (including firms, customers, regulators, academics, and NGOs) to create an ongoing, healthy dialogue. This dialogue should guide a continuing debate aimed at evaluating the implications of specific actions in customer experience management. [ABSTRACT FROM AUTHOR]

An increasing number of companies are introducing chatbot‐led contexts in service failure recovery. Existing studies are inconclusive on whether humanlike chatbot‐driven service failure recovery enhances customer loyalty. Grounding our work in phenomenological hermeneutics and utilizing frustration–aggression theory, we concentrate on the historical circumstance and the participatory nature of understanding customers' chatbot‐driven interactions and loyalty. We conducted 47 in‐depth interviews with millennials from four countries (United States, France, Italy, and the United Kingdom). By analyzing interview data through thematic analysis, our study offers two significant contributions. First, through thematic analysis, we define the dynamics occurring between customers and chatbots in a service recovery journey, such as customers' priorities and expectations. Second, we present a chatbot‐led service failure recovery typology framework that identifies four types of customers based on their interactions with a chatbot and their emotions, specifically frustration and aggression, and the effects of the interactions on their brand loyalty and intention to use chatbots. The identification of four customer types can help managers shape strategies to effectively turn negative customer experiences into opportunities to strengthen their loyalty, such as making more than one touchpoint available (human and chatbot). Our study shows that customers' emotions, specifically frustration and aggression, affect not only customer loyalty but also technology adoption. The concluding section suggests future avenues for research in the service recovery literature. [ABSTRACT FROM AUTHOR]

Amid product scandals, corporate malpractices, and the proliferation of misinformation, consumers are becoming increasingly more skeptical about the brands they purchase. Transparency is often hailed as a strategic imperative to reassure consumers and increase brand trust; however, its deployment can be complex. With additional transparency, consumers may become overloaded with information, and the brand may be exposed to unwanted external scrutiny. Consequently, before disclosing strategically sensitive information, brands need more insight into how increasing transparency might translate into strategically desirable consumers' brand evaluations. Addressing this need, we examine how transparency initiatives translate into consumers' evaluation of brands by delineating the dimensions of the perceived brand transparency construct, namely, observability, comprehensibility, and intentionality. Next, we use this conceptualization to develop and validate a scale to measure perceived brand transparency. We close with a brand transparency research agenda, as well as practical guidance for managers considering investing in strategic transparency initiatives. [ABSTRACT FROM AUTHOR]

In today's data-driven marketing landscape, predicting customer responses to marketing campaigns is essential for optimizing both engagement and Return On Investment (ROI). This study aims to develop a predictive model using a Decision Tree (DT) to identify key factors influencing customer behavior and improve campaign targeting. The methodology involves building the DT model, initially achieving an accuracy of 87.3%. However, the model faced challenges with precision and recall due to class imbalance. To address this, a resampling technique was applied, which significantly improved model performance, increasing recall from 44% to 83.1% and the F1-score from 49% to 74.2%. Key influential features identified include the recency of a customer's purchase, their duration as a customer, and their response history to previous campaigns. This study demonstrates the practicality and interpretability of the DT model, offering actionable insights for marketing professionals seeking to enhance campaign effectiveness and customer targeting. [ABSTRACT FROM AUTHOR]

Introduction: In the modern competitive landscape of football, clubs are increasingly leveraging data-driven decision-making to strengthen their commercial positions, particularly against rival clubs. The strategic allocation of resources to attract and retain profitable fans who exhibit long-term loyalty is crucial for advancing a club's marketing efforts. While the Recency, Frequency, and Monetary (RFM) customer segmentation technique has seen widespread application in various industries for predicting customer behavior, its adoption within the football industry remains underexplored. This study aims to address this gap by introducing an adjusted RFM approach, enhanced with the Analytic Hierarchy Process (AHP) and unsupervised machine learning, to effectively segment football fans based on Customer Lifetime Value (CLV). Methods: This research employs a novel weighted RFM method where the significance of each RFM component is quantified using the AHP method. The study utilizes a dataset comprising 500,591 anonymized merchandising transactions from Amsterdamsche Football Club Ajax (AFC Ajax). The derived weights for the RFM variables are 0.409 for Monetary, 0.343 for Frequency, and 0.248 for Recency. These weights are then integrated into a clustering framework using unsupervised machine learning algorithms to segment fans based on their weighted RFM values. The simple weighted sum approach is subsequently applied to estimate the CLV ranking for each fan, enabling the identification of distinct fan segments. Results: The analysis reveals eight distinct fan clusters, each characterized by unique behaviors and value contributions: The Golden Fans (clusters 1 and 2) exhibit the most favourable scores across the recency, frequency, and monetary metrics, making them relatively the most valuable. They are critical to the club's profitability and should be rewarded through loyalty programs and exclusive services. The Promising segment (cluster 3) shows potential to ascend to Golden Fan status with increased spending. Targeted marketing campaigns and incentives can stimulate this transition. The Needs Attention segment (cluster 4) are formerly loyal fans whose engagement has diminished. Re-engagement strategies are vital to prevent further churn. The New Fans segment (clusters 5 and 6) are fans who have recently transacted and show potential for growth with proper engagement and personalized offerings. Lastly, the Churned/Low Value segment (clusters 7 and 8) are fans who relatively contribute the least and may require price incentives to potentially re-engage, though they hold relatively lower priority compared to other segments. Discussion: The findings validate the proposed method's utility through its application to AFC Ajax's Customer Relationship Management (CRM) data and provides a robust framework for fan segmentation in the football industry. The approach offers actionable insights that can significantly enhance marketing strategies by identifying and prioritizing high-value segments based on the club's preferences and requirements. By maintaining the loyalty of Golden Fans and nurturing the Promising segment, football clubs can achieve substantial gains in profitability and fan engagement. Additionally, the study underscores the necessity of re-engaging formerly loyal fans and fostering new fans' growth to enable long-term commercial success. This methodology not only aims to bridge a research gap, but also equips marketing practitioners with data-driven tools for effective and efficient customer segmentation in the football industry. [ABSTRACT FROM AUTHOR]

Purpose: This research explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in brand management. It aims to understand how AI technologies can optimize brand awareness, enhance personalized communication, and improve brand equity measurement, thereby redefining brand management strategies in the digital era. Design/methodology/approach: A systematic literature review was conducted, analyzing research studies published between 2014 and 2024. The methodology focused on articles that delve into the integration of AI and ML within brand management contexts. This approach was chosen to consolidate and synthesize a broad spectrum of findings on AI’s impact on brand management practices. Findings: The review identified significant enhancements in brand management facilitated by AI, particularly in consumer engagement and customization of consumer interactions through data-driven insights. AI's capability to analyze big data has enabled more precise consumer segmentation and targeting, thereby influencing brand loyalty and overall brand equity positively. Research limitations/implications: The primary limitation of this study is its reliance on data from articles indexed only in Scopus, potentially omitting relevant studies published in other languages or databases. Future research should expand the scope to include these sources and explore empirical validations of the proposed theoretical impacts of AI on brand management. Practical implications: This research highlights the practical applications of AI in improving brand management strategies, offering insights into effective AI integration into marketing practices. Businesses are encouraged to adopt AI-driven tools for better market segmentation, consumer behavior predictions, and enhanced customer relationship management. Social implications: The findings could influence public attitudes towards brand interaction by fostering greater acceptance of AI in consumer relations. The research supports enhanced corporate social responsibility through AI's ability to provide more targeted and meaningful consumer interactions. Originality/value: This paper contributes to the academic and practical understanding of AI’s role in brand management by synthesizing existing research and identifying future research directions. It is valuable to academicians, marketing professionals, and policymakers interested in the implications of AI technology in brand strategies. [ABSTRACT FROM AUTHOR]

The healthcare industry is undergoing a paradigm shift towards patient-centered care. In this competitive environment, patient satisfaction and loyalty are crucial for hospitals. While patient satisfaction is routinely measured, its precise relationship with loyalty remains unclear, as existing studies have yielded mixed results. Other determinants aside from patient satisfaction may also influence patient loyalty. By identifying these key determinants, hospitals can develop targeted strategies to enhance patient satisfaction and promoting patient loyalty, ultimately securing a competitive advantage. Objective: This systematic literature review aims to explore the current research on factors influencing outpatient loyalty to hospitals. Method: This systematic review uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search strategy was used to identify relevant studies in PubMed, ProQuest, and ScienceDirect. The search timeframe encompassed publications between January 2019 and April 2024. A set of predefined inclusion and exclusion criteria were applied, resulting in the identification of 17 articles relevant for further analysis. Result: Patient satisfaction emerged as a key driver of loyalty, positively influenced by factors beyond service quality. These factors include trust in the hospital, word of mouth, hospital image, strong patient-physician relationships, patient convenience, patient motives, switching cost, customer relationship management, and staff kindness and consideration. Conclusion: Patient loyalty is influenced by a multitude of factors extending beyond service quality and satisfaction. Building trust, nurturing patient relationships, optimizing the patient experience, and offering value are crucial considerations. Tailoring strategies to specific patient demographics and cultural contexts are essential. [ABSTRACT FROM AUTHOR]

This study aims to analyze the influence of perceived service quality, perceived reputation, and social presence on purchase intention, with trust as a mediating variable, on Lazada users in DKI Jakarta. Purchase intention is a consumer's intention to make a purchase that is influenced by various factors, including perceived service quality, perceived reputation, and social presence on an e-commerce platform. Consumer trust is an important key in mediating the relationship between these variables and purchase intention. This study uses a quantitative method with data collection through questionnaires distributed to Lazada users in DKI Jakarta. Data analysis was carried out using multiple regression models and path analysis to evaluate the role of trust in mediating the relationship between independent variables and purchase intention. The results of the study indicate that perceived service quality, perceived reputation, and social presence significantly influence purchase intention through trust. Consumer trust is proven to be an important mediator, which strengthens the influence of the three independent variables on purchase intention. These findings provide implications for e-commerce platform managers in improving service quality, building a good reputation, and strengthening social presence to increase consumer trust and purchase intention. [ABSTRACT FROM AUTHOR]

This research aims to examine the effect of Key Opinion Leader (KOL) and Customer Relationship Management (CRM) on customer loyalty with customer satisfaction as a mediating variable in IBOX customers. This research uses a quantitative method with an explanatory research approach. The sample used was 150 respondents who were IBOX customers in the Jabodetabek area. The sampling technique used was purposive sampling with certain criteria. Data collection was carried out through online questionnaires distributed to respondents. Data analysis was performed using Structural Equation Modeling (SEM) with the help of AMOS (Analysis of Moment Structures) version 24 software. [ABSTRACT FROM AUTHOR]

The insurance industry is one of the largest financial areas that due to its service nature, customers can create different values, and the marketing efforts of insurance companies in attracting and retaining different groups of customers have led them to implement various loyalty programs, in which one of the criteria for grouping customers is the customer lifetime value (CLV). In this regard, this research has been conducted with the aim of designing and evaluating the loyalty program of insurance industry customers based on their lifetime value as an applied mixed exploratory, and cross-sectional research. The statistical population of the classification stage was 800 customers registered in the database of Shiraz insurance branches in 2018, and the statistical sample for prioritizing loyalty programs was 40 customers who were randomly selected using the Morgan table and based on access and willingness to participate in the study, the sample was completed. In the first stage, through systematic review, 28 loyalty programs were identified, and 15 valid programs were extracted using the content validity ratio (CVR), and these programs were categorized, and the Kappa index was used to assess categorization reliability. Therefore, in the second stage, the weights of LRFM indexes were determined with the analytic hierarchy process (AHP) and then the customers were classified into four groups of keys, intermittent, prodigal, and uncertain clusters customers using their data in insurance branches databank. Finally, the customers' priority of identified programs in each group was determined using the fuzzy best–worst method (FBWM). The findings show that from the perspective of customers, intangible and long-term programs are more attractive to key customers, while for intermittent customers, long-term tangible loyalty programs, for uncertain customers, immediate loyalty programs, and for prodigal customers, intangible loyalty programs are more attractive. The results of this study can be used in the formulation of loyalty programs in the insurance industry, in such a way that suitable loyalty programs are considered for each group of customers depending on the customers' CLV. Such as in designing loyalty programs, the tangibility and time horizon must be considered. Also, each group of programs must be suitable to customers' CLV. [ABSTRACT FROM AUTHOR]

The telecom sector creates huge amounts of information every day as a result of its large customer base. Business professionals and decision-makers emphasized that maintaining existing clients is less expensive than recruiting new ones. Business analysts and customer relationship management (CRM) need to know the reasons why customers leave and the behavior patterns from earlier churn consumer’s data. Today, there is a problem with customer churn examination and prediction in the telecom industry since it is crucial for the sector to examine customer behavior to identify those who are going to stop their subscriptions. Customer retention could be increased by utilizing detection system to detect consumer behavior. Recent advancements in machine learning (ML) have made churn prediction more precise and practical. It is essential for identifying customers ready to leave using company’s products and services in the early stage. Hence in this work, predicting customers churn in telecommunication sector using Naïve Bayes (NB) model is presented. The performance of presented NB algorithm is evaluated using the parameters accuracy, precision, and sensitivity. The NB algorithm will have better performance than pervious approaches. [ABSTRACT FROM AUTHOR]

This study, anchored on attribution theory, examines the role of CSR motives and perceived CSR authenticity in influencing CRM purchase intentions in the context of CRM-oriented online retailers. This study employed integrated SEM-ANN-NCA approach to establish a model's necessity logic and determine the key predictors that impact CRM purchase intention. The findings of ANN indicated that CSR authenticity was the strongest predictor. Likewise, CSR authenticity, value-driven motive, and stakeholder-driven motive were necessary conditions (must-have) during NCA to determine CRM purchase intention. The results would provide valuable insights to practitioners, including advertising professionals, and online retailers to design effective CRM campaigns. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to contribute to previous research on customer relationships by quantitatively examining differences in the monetary benefits obtained by consumers using social and commercial relationships to make purchases from small and medium-sized enterprises (SMEs). Design/methodology/approach: Customer transaction and relationship data from an SME in the USA is used to quantitatively assess the value of different marketplace relationships in an entrepreneurial context. Tobit regression is used to empirically model and test the impact of specific relationship characteristics on customer discounts. Findings: Customers using social connections to make purchases obtain significantly larger discounts than customers using commercial connections; customers using direct connections attain significantly larger discounts than consumers using indirect connections (referrals). Interestingly, when examined by connection type, direct and indirect connections do not produce significant differences for social connections, yet they yield notable differences for commercial connections. The findings provide valuable insights to entrepreneurs for understanding and managing customer relationships. Originality/value: This study empirically demonstrates that social relationships can be both prevalent and influential in the marketplace. The methodology used to quantitatively assess the monetary value associated with different methods of engaging with SMEs allows objective comparisons among different types of customer relationships. Quantification also allows important relationship characteristics to be empirically examined, including how the relationships compare to one another and to nonpersonal marketing activities. Ultimately, these novel contributions generate important insights to help marketers and entrepreneurs better understand customer relationships. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19). Design/methodology/approach: A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities. Findings: Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed. Research limitations/implications: The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample. Practical implications: Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach. Originality/value: This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation. [ABSTRACT FROM AUTHOR]

This research aims to determine the influence of product quality and innovation on customer relationship management through prices in Payung Geulis creative industry in Kahuripan village, Indihiang, Tasikmalaya. This research implies an explanatory survey design using Structural Equation Modeling (SEM) with Partial Least Squares (PLS) as the analysis method. Data collection was carried out through questionnaires distributed to 200 customers. The findings reveal that product quality and innovation significantly influence prices and impact customer relationship management--higher product quality results in greater customer satisfaction and emotional attachment to the price. Additionally, innovation has a positive impact on customer engagement and strengthens the relationship with price. Moreover, price significantly influences customer relationship management, since price reduces resistance to changes in product quality and innovation in customer relationship management. Furthermore, this research shows that the impact of product quality and innovation influences customer relationship management indirectly through price. [ABSTRACT FROM AUTHOR]

The present study reviewed gratitude at work articles published in Scopus database using bibliometric analysis to examine growth in gratitude at work publications and citations, most cited publications, most influential authors, articles, publishers, universities and countries. The extracted data were analysed and visualised with the help of two analytical tools, that is, the Scopus analysis and VOSviewer (version 1.6.16). It identified 273 publications referring to gratitude at work in title, abstract, or keywords. The keyword co‐occurrence highlighted four major clusters of workplace gratitude research: Psychological studies, Customer relationship management, Employees, and Ethics and spirituality. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the effect of social disruption on the use of technologies for digitizing business-to-business (B2B) processes. The aim is to assess how digitalization technologies (DT) may impact corporate performance (CP) in B2B settings. Design/methodology/approach: The methodology is based on a questionnaire survey in Norway, and a deductive research design. A total of 216 usable questionnaires out of 356 were returned, generating a response rate of 60.6%. Findings: This study shows that there is an effect of social disruption on DT (such as digital communication tools, social media and customer relationship management systems) in B2B settings that may impact CP. Research limitations/implications: This study indicates that the use of technologies to digitize B2B processes may enhance CP when social disruption occurs. Practical implications: This study offers insights to companies that need help in adapting their business processes to the changing social and technological environment. This study also highlights the importance of digitalization for business survival in the marketplace and society. Originality/value: This study sheds light on the effect of social disruption on DT and provides opportunities for managing CP. [ABSTRACT FROM AUTHOR]

The role and function of Public Relations (PR) in carrying out tasks in the marketing sector of a digital service company, namely Animation Studio. Nowadays, managing relationships with customers is more familiarly known as Customer Relationship Management (CRM). In CRM management, a series of flows have been arranged as mentioned by Buttle, namely analysis, resource development and implementation. The data obtained in this research is the result of observation, documentation and interviews with Marketing and CEO while carrying out research at the Animation Studio. It was found that analyzes covering portfolio and customer intimacy had been run on a strong basis and implemented quite well. Likewise, the development of resources that have been designed by the CEO to develop the network and add value propositions has been carried out systematically according to procedures. As for the implementation of CRM in Animation Studio, although it is quite good, it still needs to be developed further in terms of the process to further perfect CRM within the company. [ABSTRACT FROM AUTHOR]

Although chatbots are used a lot for customer relationship management (CRM), there needs to be more data security and privacy control strategies in chatbots, which has become a security concern for financial services institutions. Chatbots gain access to large amounts of vital company information and clients' personal information, which makes them a target of security attacks. The loss of data stored in chatbots can cause major harm to companies and customers. In this study, STRIDE (viz. Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege) modelling was applied to identify the data security vulnerabilities and threats that pertain to chatbots used in the insurance industry. To do this, we conducted a case study of a South African insurance organisation. The adopted methodology involved data collection from stakeholders in the insurance organisation to identify chatbot use cases and understand chatbot operations. After that, we conducted a STRIDE-based analysis of the chatbot use cases to elicit security threats and vulnerabilities in the insurance chatbots in the organisation. The results reveal that security vulnerabilities associated with Spoofing, Denial of Service, and Elevation of privilege are more relevant to insurance chatbots. The most security threats stem from Tampering, Elevation of privilege, and Spoofing. The study extends the discussion on chatbot security. It fosters an understanding of security threats and vulnerabilities that pertain to insurance chatbots, which is beneficial for security researchers and practitioners working on the security of chatbots and the insurance industry. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While service providers strive to maintain customer relationships, a nontrivial number of customers downgrade their services, something that has been particularly true during the post-pandemic period or economic recession. Studying downgrade behavior is vital because it damages the bottom-line performance of service providers and reflects a reduced customer commitment. Unlike previous studies, we further divide downgrade behavior based on whether there is a change in the product category, that is, a downgrade to a lower-priced service option within the same product category ("pure downgrade") versus a downgrade to a lower-priced service option in a different product category ("hybrid downgrade"). An analysis of customer data collected from a major telecommunications company shows fundamental differences in the determinants and consequences of these two downgrades. Transaction-related variables, such as service usage, have a significantly stronger positive effect on the likelihood of hybrid downgrade than on that of pure downgrade. Conversely, relationship-related variables like relationship length have an inverted U-shaped effect on pure downgrade but barely affect the likelihood of hybrid downgrade. Interestingly, customers who engage in pure downgrade are more likely to churn than those who engage in hybrid downgrade. The empirical findings offer valuable insights on customer relationships and churn management. [ABSTRACT FROM AUTHOR]

In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into the field of customer relationship management (CRM), bringing benefits as well as difficulties to businesses. AI, particularly in the context of CRM, employs machine learning (ML) and deep learning (DL) techniques to extract knowledge from data, recognize trends, make decisions, and learn from mistakes with minimal human intervention. Successful firms have effectively integrated AI into CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots and virtual assistants, and voice and speech recognition. AI-driven chatbots, one of the AI-powered CRM systems, arose as a disruptive approach to customer service, and as such, unfolded with economic and managerial ramifications in CRM. Given the literature's focus on other AI-driven systems, there is an obvious need for an investigation of industry applications and the implications of AI-driven chatbots in CRM. The purpose of this study is to explore and elucidate the economic and managerial implications of AI-powered chatbots within CRM systems. This investigation aims to provide a comprehensive understanding of how these technologies can enhance customer interactions, streamline business processes, and impact organizational strategies. To reach this goal, this study conducts a comparative qualitative analysis based on many interviews with experts and contributors in the field. Interviews with CRM specialists yielded insights into the use of AI-driven chatbots in CRM and their impact on the industry. The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance. In addition, AI chatbots have proven useful in a variety of industries, including retail and tourism. Nonetheless, there were limitations to its usage in the healthcare system, particularly in terms of ethical problems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

From the system view of organisations, this study investigated how client relationship management (CRM) strategy responses of architectural firms to market conditions influence their firm performance. Previous studies have proposed empirical models that explored this relationship in several organisational contexts, but there is a dearth of studies that explained the link between CRM strategy and firm performance in the context of architectural firms. The empirical model of the current study closed this gap by explaining how the CRM strategy features used by architectural firms influence their firm performance. A questionnaire survey of 195 samples of randomly selected architectural firms was conducted in Lagos, Nigeria. 82 copies of the questionnaire (42% response rate) were retrieved and the nominal and ordinal data obtained were subjected to descriptive, factor and categorical regression analyses using Statistical Product and Service Solutions (SPSS) software (IBM SPSS Statistics 23). The regression analysis revealed that client collaboration, client communication and service customisation dimensions of CRM strategy had a statistically significant influence on the firms' overall performance. The model of this study contributed to the empirical literature by revealing how the strategic behaviour of architectural firms in the industrial market optimizes their performance. Managerial implications and areas for further studies were stated. [ABSTRACT FROM AUTHOR]

Customer relationship management is complicated by a lack of understanding of customer attributes and needs. Transformational and inventive techniques are needed to improve customer service. WhatsApp-based AI Chatbots can improve academic service efficiency and quality. The platform's usability and interaction with customer management systems are the main reasons. By adopting the AI Chatbot, clients and internal users will receive consistent and correct responses. Qualitative interviews and survey analysis are used to collect data for the study. Qualitative interviews are utilized for qualitative analysis, while survey analysis validates research survey data. The report reveals 83.8% customer satisfaction and 75.3% customer loyalty. The study found that the chatbot's ability to minimize service time greatly affected customer satisfaction. Due to their consistency and speed, chatbots in customer care have improved consumer satisfaction. Customers like chatbots' speed and efficiency, which allow them to get help immediately. The chatbot's ability to handle common questions and tasks frees up staff to handle more complex and urgent issues, improving service. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the research trends in fashion industry during the coronavirus disease 2019 (COVID-19) pandemic. Besides, it also provides an overview on the new marketing and operational strategies, and reveals the corresponding business challenges of a footwear enterprise in Hong Kong during the COVID-19 pandemic. Design/methodology/approach: A comprehensive literature review is first conducted to identify the research trends in fashion industry during the COVID-19 pandemic. A qualitative exploratory case study is then used to illustrate how a footwear enterprise has coped with the COVID-19 pandemic. Findings: The case study has showed that omni-channel retailing, collaboration with e-tailers, quick response system and mixed production strategy are adopted in the targeted case during the COVID-19 pandemic. Besides, the targeted case has also faced the challenges in the areas of sales, customer relationship management, and demand forecasting and inventory planning during the COVID-19 pandemic. Originality/value: This study provides managerial insights on the real practices used to deal with the COVID-19 pandemic and proposes various academic future research directions in fashion industry based on the real-world observations. [ABSTRACT FROM AUTHOR]

Electronic Customer Relationship Management (e-CRM) systems have become instrumental in reshaping the landscape of customer engagement and service efficiency in the banking industry. This research paper delves into the implementation and impact of e-CRM in the context of the State Bank of India (SBI), one of the world's largest and oldest banking institutions. SBI's adoption of e-CRM is examined as a case study, providing insights into the strategic decisions, challenges, and outcomes associated with this transformation. The study employs a mixed-method approach, combining qualitative and quantitative data collection methods. Data is gathered through interviews, surveys, and analysis of archival information to evaluate the implementation process and its subsequent effects on customer engagement and service efficiency. Through this comprehensive analysis, this paper highlights the critical role of e-CRM in shaping the modern banking experience. Preliminary findings suggest that SBI's e-CRM implementation has had a profound impact on enhancing customer engagement, resulting in increased satisfaction and improved interactions. Furthermore, the paper reveals improvements in service efficiency, including reduced response times, decreased error rates, and enhanced cost-effectiveness. The research identifies factors influencing the success of e-CRM implementation, emphasizing the significance of organizational culture, technological infrastructure, and employee training. This research not only provides valuable insights for the banking industry but also offers lessons learned and best practices for organizations considering the adoption of e-CRM. The implications of this study transcend the specific case of SBI, serving as a valuable reference for institutions aiming to remain competitive and responsive in an increasingly digitized and customer-centric banking environment. [ABSTRACT FROM AUTHOR]

This study explores the impact of negative reviews and responses on rapport management, synthesizing current research on this topic. Employing formulated research questions and statistical methods like chi-square tests, this study systematically analyzed collected data both quantitatively and qualitatively. Results indicate significant differences in response voices-accommodative, formalistic, and defensive-among various interpersonal relationship management styles when addressing negative reviews. These findings emphasize the complexity of managing interpersonal relationships amidst criticism, offering theoretical insights and practical guidance for improving customer rapport management in industries like hospitality. By examining how different management voices respond to negative reviews, this study contributes a fresh perspective to enhancing customer relationship practices in service sectors. [ABSTRACT FROM AUTHOR]

The present research purpose has been dynamic modeling of customer relationship management in e-banking in Sepeh Bank and merged banks, and the conceptual framework identified in the case study was used. The statistical population is the experts in Sepah Bank's customer affairs, and a sample of 14 people has been selected through theoretical sampling method utilizing purposeful techniques. To measure the relationship between dimensions and indicators, fuzzy cognitive map was used in the form of pairwise comparison matrix questionnaire, and the reliability and validity of the questionnaires were confirmed by the test-retest method and the content validity with Lawshe method. For simulation in a certain period of time and measuring the validity of the model, Bidirectional Associative Memory(BAM) Neural Networks have been used in the FCM Expert software. The research findings show that "Customer Relationship Development" is the most central and influential and "Customer Retention" is the most affected dimension, and the mutual influence between "Customer Identification" and "Customer Relationship Development" is significant. Also, the "Customer Attraction" has the greatest impact on the "Customer Retention & Customer Relationship Development" and the "Customer Reach and Acquisition" has the greatest impact on the "Customer Attraction & Customer Retention". Based on these results, it is recommended that Sepah Bank managers pay special attention to customer attraction through branding and the implementation of advertising campaigns at contact points, and despite the low impact of the "Customer Relationship Review", its significant impact on the most central dimension, i.e. "Customer Relationship Development" should not be neglected. [ABSTRACT FROM AUTHOR]

Purpose: In today's technology-driven landscape, social media has become essential for organizations aiming to stay competitive. It serves as a powerful tool for managing customer relationships and maintaining close connections with clients. This paper explores the impact of social media on the evolution of customer relationship management (CRM), focusing on the importance of social networks and their increasing prominence. Adapting to this growing trend of social media integration is crucial for organizations seeking to thrive in the modern market. Design/Methodology/Approach: The integration of social media into marketing strategies is highlighted as a key component in enhancing CRM practices. With CRM centered around prioritizing the customer, marketing efforts are increasingly tailored to this focus. Through case studies and survey data, the paper investigates the shift from traditional CRM to social CRM. Findings: Despite its emergence as a relatively new field, there is significant interest in how CRM can evolve alongside social media. The research underscores various ways in which companies can leverage social media to boost sales, profitability, advertising efforts, customer acquisition, retention, and loyalty. Practical Implications: Adapting to this growing trend of social media integration is crucial for organizations seeking to thrive in the modern market. [ABSTRACT FROM AUTHOR]

This study aims to analyze the implementation, implementation process, obstacles, and impacts of Electronic Customer Relationship Management (E-CRM) on the formation of Positive Word of Mouth at PT Mandiri Utama Finance. This study uses a qualitative approach with a descriptive research method. The research approach is carried out with case study approach. This research is expected to provide solutions to businesses at the company in determining strategic steps as well as making regulations or implementing appropriate policies, and providing input for the next implementation of E-CRM. The results show that the company's overall satisfaction was 6.35 or equivalent to 96%. The implementation process and obstacles in forming a positive word of mouth are identified as the main obstacles in the implementation process. The use of social media and other digital media plays an important role in the development of positive words of mouth. [ABSTRACT FROM AUTHOR]

Interactions between service employees and customers at check‐in shape customers' first impressions and lead to their future behavioral intentions. Even though customer relationship management (CRM) relies heavily on the hospitality check‐in experience, research is scarce on its impacts and the moderating effect of loyalty membership programs. Based on the broaden‐and‐build theory, the current study examined customer check‐in significance associated with their outcomes and compared two groups to determine if loyalty programs have a meaningful impact. The findings confirmed the importance of the check‐in experience and the moderating effect of loyalty membership was significantly related to customers' perceptions. [ABSTRACT FROM AUTHOR]

In this competitive world, companies should sustain good relationships with their consumers. CRM (customer relationship management) program can improve the company's customer satisfaction; to satisfy customer need different processes and technique are established to make the CRM more effective. This research is proposed to determine the relationship between customer loyalty and retention. Also, this research examines the impact of Customer Relationship Management (CRM) on Customer Satisfaction. The target population of this study is customers of the tourism industry in India (n = 300). Then, regression analysis is carried out in order to discover the link between the variables. This study result shows that service quality and employee behavior of customer need and satisfaction with the effect of different significant of positive relation of both the variables. To make the customer satisfied and to retain their company the CRM should be strong and reliable with the consumers. CRM plays a vital role in increasing market share, high productivity, improving in-depth customer knowledge, and customer satisfaction to increase consumer loyalty to the company to have a clear view of who is their customer, what are the need of their customer and how can satisfy their needs and wants their customers. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) integration into Customer Relationship Management (CRM) within enterprise information systems (EIS) has provided businesses with transformative opportunities to improve customer engagement and operational efficiency. Due to the accelerating momentum of AI technologies and the digital evolution prompted by the COVID-19 pandemic, there has been an increase in AI-based CRM research, yet comprehensive systematic studies are still lacking. This study presents an overview of the historical developments, state-of-the-art and, existing research domains in the literature, utilizing bibliometric analysis of 810 scholarly articles over the last two decades. The literature has been categorised into four main clusters; (1) Data-Driven CRM Strategies; (2) Application of AI techniques to CRM activities; (3) Strategic implementation of AI into CRM; (4) Customer experience (CX) through AI-based CRM. [ABSTRACT FROM AUTHOR]

Purpose; The study conducted a comprehensive analysis to determine the influence of customer engagement on customer retention at Guaranty Trust Bank Plc, centring on the Asaba region in Delta State, Nigeria. Method; This research employed a cross-sectional survey methodology and utilised a questionnaire as the primary data collection tool, drawing responses from a total of 417 participants, consisting of both bank staff and customers. The collected data was subjected to rigorous validation and reliability testing. The research utilised inferential statistics, including the Sampled Paired Test and Cronbach Alpha, with the aid of SPSS version 25, to evaluate the hypotheses. Result; The results uncovered significant positive impacts of various forms of customer engagement on different aspects of customer behaviour. Limitation; This study was limited to only Guaranty Trust Bank plc in Asaba, Delta State Nigeria, and also explored the content scope of customer engagement and retention. Contributions; The study has contributed to the wealth of knowledge in theory and practices, and it has also given insight into why commercial banks in Nigeria should hold firmly on the strategies of customer engagements. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Marmara University Social Sciences Institute / Öneri is the property of Marmara University, Institute of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Theoretical background: The popularity of AI in recent years has led to its integration with various industrial sectors. This spectrum of AI applications has brought numerous topics that are underexplored and need to be taken up. One such area is the integration of AI with customer relationship management (CRM) systems. In consequence, this research is valuable for the scientific community. Purpose of the article: The purpose of the research is to conduct scientometric analysis in the field of AI in CRM and to identify the most crucial areas of research, identify motor and niche themes in the AI-CRM and indicate future research trends. Research methods: The methodology was divided into three parts: data collection, descriptive analysis, scientometric analysis. In the research, “R programming” and “Biblioshiny” were used to conduct scientometric analysis. This made it possible to generate charts and a table based on gathered data and then analyse the results. Main findings: The results show there is a growth in AI-CRM systems subject since 2019. Keywords like “CRM”, “public relationship” or “data mining” were often used in research articles. Future research trends can be define among others: acceptance and trust for AI powered technology, impact on companies’ environment or relationship with customer. [ABSTRACT FROM AUTHOR]